Huawei Wireless Factory Solution for Manufacturing

whitePaper | October 28, 2022

Manufacturing companies can integrate all the connected resources of a process and their various business applications – enterprise resource planning (ERP), manufacturing execution system (MES), product lifecycle management (PLM), and warehouse management system (WMS). They can also connect their assets, supply chain partners, and even customers through networks and the cloud.

whitePaper | November 12, 2019

Today’s economies are changing dramatically, driven by developing and emerging markets, innovative products and business models, and the accelerated rise of advanced technologies, such as machine learning and artificial intelligence. Globally, the automotive and manufacturing segments have seen record-setting profits over the past decade-and also transformative change.

whitePaper | August 3, 2020

Edge computing is one of today's leading disruptive digital technologies. Until recently, it was overshadowed by the Cloud with its power, scalability, and as-a-service platform capabilities. However, there is an increasing realization that both have their place in the Industrial Internet of Things (IIoT) environment and that Edge offers a number of capabilities and advantages that make it a key player in the new Operational Technology (OT) Ecosystem, as well as in evolving Operational Architectures.

whitePaper | November 30, 2022

The promise of metal additive manufacturing (AM) to completely transform how core parts are produced has largely been just that: a promise. The ability to 3D print parts has been deployed across countless industries, but limitations within conventional iterations of metal AM have relegated the technology to use primarily in prototyping or niche use cases.

whitePaper | March 17, 2022

Manufacturing industry professionals must corral and understand massive amounts of data from many systems to drive operational efficiencies, higher levels of service, supply chain resiliency, and ultimately a complete view of the business. Having the ability to explore the impact of and interplay among changes in production efficiency, product quality, customer demand, and service excellence isn’t possible without visibility into data and meaningful data analytics. This “big data” approach to manufacturing operations and supply chain optimization can deliver big results and is at the heart of the digital transformation initiatives of many industrial companies.

whitePaper | July 7, 2022

Production scheduling primarily deals with arranging different jobs in various machines based on the timelines for each job. There can be numerous combinations of schedules possible. Production schedules can be designed to maximize the capacity utilization, minimize idle time of the machine, earliness, overall costs etc. Manual design of a schedule can be a tedious and time consuming task. There are a lot of dependencies which the scheduler needs to tweak based on a single change. Generating a production schedule targeting a specific objective function can be done using mathematical models. Mathematical models involve formulating the production scheduling problem as an optimization model wherein there is a certain objective function which needs to be optimized based on a set of constraints. Mathematical models generally outperform manually designed schedules which involve a lot of inter-dependencies among the jobs. Due to improvement in computational power, developing a production schedule using mathematical models is relatively quick and efficient.